Energy-Efficient Collaborative Communication for Optimization Cluster Heads Selection Based on Genetic Algorithms in Wireless Sensor Networks

Ma, Wen-Loong; Chen, Yuan; Wei, Wei; Hei, Xinhong; Ma, Jianfeng

doi:10.1155/2015/396121

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…The exploration and exploitation are two major components in each meta-heuristic algorithm search process. In recent years, several nature-inspired biological algorithms such as genetic algorithm (GA) [35], cuckoo search (CS) [36], firefly algorithm (FA) [37] have been proposed to solve optimization problems. The main advantages of these algorithms include the low probability of entrapment into local modes and faster convergence due to appropriate information-sharing during optimization.…”

Section: B Bio-inspired Computingmentioning

confidence: 99%

FFRP: Dynamic Firefly Mating Optimization Inspired Energy Efficient Routing Protocol for Internet of Underwater Wireless Sensor Networks

et al. 2020

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Energy-efficient and reliable data gathering using highly stable links in underwater wireless sensor networks (UWSNs) is challenging because of time and location-dependent communication characteristics of the acoustic channel. In this paper, we propose a novel dynamic firefly mating optimization inspired routing scheme called FFRP for the internet of UWSNs-based events monitoring applications. The proposed FFRP scheme during the events data gathering employs a self-learning based dynamic firefly mating optimization intelligence to find the highly stable and reliable routing paths to route packets around connectivity voids and shadow zones in UWSNs. The proposed scheme during conveying information minimizes the high energy consumption and latency issues by balancing the data traffic load evenly in a large-scale network. In additions, the data transmission over highly stable links between acoustic nodes increases the overall packets delivery ratio and network throughput in UWSNs. Several simulation experiments are carried out to verify the effectiveness of the proposed scheme against the existing schemes through NS2 and AquaSim 2.0 in UWSNs. The experimental outcomes show the better performance of the developed protocol in terms of high packets delivery ratio (PDR) and network throughput (NT) with low latency and energy consumption (EC) compared to existing routing protocols in UWSNs.INDEX TERMS Internet of Underwater Things, bio-inspired routing, firefly mating optimization, underwater wireless sensor network, routing protocol. I. INTRODUCTIONThe oceans contain about 96.5% of all earth water is extremely important for the survivability of human life since it provides nourishment, natural resources, ways of transportation, greater defense margin and several other benefits. However, a vast portion of the oceans around 95% is still unexplored due to the lack of appropriate acoustic communication technologies. Recently, significantThe associate editor coordinating the review of this manuscript and approving it for publication was Ilsun You . advancements in the Internet of underwater things (IoUT) technology have facilitated the exploration process of the oceans by connecting various ubiquitous sensor devices to provide reliable and efficient data collection in UWSNs [1], [2]. The acoustic sensor nodes (ASNs) in underwater acoustic sensor networks (UASNs) have the potentials to monitor real-time underwater events with failure detection and self-organizing capabilities. Therefore, UASNs have received significant attention in a variety of ocean monitoring applications, such as navigation assistance, tactical surveillance, mine recognition, underwater pollution analysis and

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Section: B Bio-inspired Computingmentioning

confidence: 99%

FFRP: Dynamic Firefly Mating Optimization Inspired Energy Efficient Routing Protocol for Internet of Underwater Wireless Sensor Networks

et al. 2020

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“…To resolve the energy shrinking tricky produced by the community of the bowl, which is troubled with weighty relay traffic via multi flaw statement and inclines to die former, a new energy-efficient cooperative statement typical is planned based on inherent algorithms in wireless sensor networks (WSNs) [28]. By scenery the inception value for a new generation conclusion function, the planned algorithm would be accomplished of arbitrating whether the sensor bulges can be a cluster head.…”

Section: Literutre Reviewmentioning

confidence: 99%

Genetic algorithm in Wireless Networking: A Review

Abualigah¹

2019

IJSAIT

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The genetic algorithm (GA) is a powerful metaheuristic inspired by the genes selection behavior of the natural human. In addition, it is clear, easy to use, soft, and has a special ability to discover the right balance between the exploration and exploitation strategies during the search, which points to positive convergence. Therefore, the genetic algorithm has recently obtained a very important research concern with the large public from several areas in a very short time. The genetic algorithm has proved its efficiency in wireless network applications since its proposal. In this paper, the genetic algorithm, its operators, and the related wireless networks works that utilized this algorithm are presented.

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“…A fuzzy interference technique called Mamdani method to input the parameters of fuzzy logic controller are node density, node centrality and node energy. Wei-gang Ma et al address a novel method EECCA [9] for the objective of optimization in selecting better nodes and depends on the overall distribution of nodes with guaranteed stability of the number of cluster heads. Genetic algorithm filter these problems by setting the threshold value and concentration of nodes in the local area can be avoided and reduce the energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Reinforce Coverage Area of Sensor Mote in Wireless Ad-Hoc Networks Using Genetic Algorithm

Hemalatha¹,

Srinivasan²

2018

IJET

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Wireless sensor based communication is everlasting growing sector within the industry of communication. In WSN improving the life expectancy of the network depends on the energy dissipation of senor devices. Diminishing the energy dissipation of sensor device will enhance the lifetime and device failure which helps in better availability and coverage area of sensor network. One of the dynamic research fields in wireless sensor network is that of coverage. Coverage can be defined as how well each point of interest is monitored by sensor network. In this paper, we investigate the cluster head selection issue, particularly focusing on applications where the upkeep of full network coverage is the fundamental prerequisite. Coverage maintenance for extended period is a pivotal issue in wireless sensor network because of the constrained inbuilt battery in sensors. Coverage maintenance may be prolonged by utilizing the network energy efficiently, by keeping an adequate number of sensors in sensor covers. The clustering algorithm is a solution to reduce energy consumption which can be helpful to the scalability and network lifetime. Assuming serious energy rebalancing with additional clustering algorithm, a Genetic algorithm (GA) based clustering algorithm which evaluates the fitness function by considering the two major parameters distance and energy has been proposed in this paper. Simulation result shows that the proposed solution finds the optimal cluster heads and has prolonged network lifetime and maximum coverage.

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Energy-Efficient Collaborative Communication for Optimization Cluster Heads Selection Based on Genetic Algorithms in Wireless Sensor Networks

Cited by 5 publications

References 22 publications

FFRP: Dynamic Firefly Mating Optimization Inspired Energy Efficient Routing Protocol for Internet of Underwater Wireless Sensor Networks

FFRP: Dynamic Firefly Mating Optimization Inspired Energy Efficient Routing Protocol for Internet of Underwater Wireless Sensor Networks

Genetic algorithm in Wireless Networking: A Review

Reinforce Coverage Area of Sensor Mote in Wireless Ad-Hoc Networks Using Genetic Algorithm

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